{"id":2920131,"url":"https://alion.io/job/thumpn-staff-engineer-ai","title":"Staff Engineer - AI","company":{"id":21172,"name":"thumpN","domain":"thumpn.com","url":"https://alion.io/company/thumpn","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":35000,"max_usd":83000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function 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retrieval, and guardrails.\nBuild the core loop: understand intent, find the right events, and help fans book when they're ready.\nHandle the messy parts of conversation: multilingual queries, session handoffs, and recovering well when the agent gets it wrong.\nDefine what \"good\" looks like for each capability, build eval sets from real sessions, and use them to decide what ships.\nSet up tracing and debugging so bad responses can be traced back to a prompt, a tool, retrieval, or code.\nChoose models and architecture based on latency, quality, and cost, including knowing when a smaller model or a simple rule does the job.\nRaise the quality of engineering around you through reviews and examples.\nWork with product and leadership on what Shadow does next.\n\nRequirements: \n7+ years of software engineering experience, with at least 1+ year on agentic systems.\nYou've shipped an agent that takes actions, not just one that answers questions.\nHands-on with the current stack: model APIs, orchestration, RAG, structured outputs, and multi-turn state.\nYou can explain how you measured whether something you shipped actually worked.\nStrong backend skills in Python and/or TypeScript.\nYou think about latency, cost, prompt versioning, and what happens when things fail.\nYou've taken ambiguous projects from start to finish.\nYou use AI tools well in your own work and know where they fall short.\n\nNice to Have: \nLLM observability tools (Langfuse, LangSmith, Datadog LLM Observability).\nPayments, ticketing, or marketplace experience in India.\nFine-tuning or serving open-weight models.\nFamiliarity with our stack: Next.js, Go, AWS, Cloudflare.\nSkills\nBackend Development, Python, RAG, TypeScript, 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